Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030—Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

The electron dynamics in the normal state of ${\mathrm{B}\mathrm{i}}_2{\mathrm{S}\mathrm{r}}_2{\mathrm{C}\mathrm{a}\mathrm{C}\mathrm{u}}_2{\mathrm{O}}_{8+\delta }$ is studied by inelastic light scattering over a wide range of doping. A strong anisotropy of the electron relaxation is found which cannot be explained by single-particle properties alone. The results strongly indicate the presence of an unconventional quantum-critical metal-insulator transition where “hot” (antinodal) quasiparticles become insulating while “cold” (nodal) quasiparticles remain metallic. A phenomenology is developed which allows a quantitative understanding of the Raman results and provides a scenario which links single- and many-particle properties.

Inelastic light-scattering spectra of underdoped ${\mathrm{La}}_{2-x}{\mathrm{Sr}}_x{\mathrm{CuO}}_4$ single crystals are presented which provide direct evidence of the formation of quasi-one-dimensional charged structures in the two-dimensional ${\mathrm{CuO}}_2$ planes. The stripes manifest themselves in a Drude-like peak at low energies and temperatures. The selection rules allow us to determine the orientation to be along the diagonals at $x=0.02$ and along the principal axes at $x=0.10$. The electron-lattice interaction determines the correlation length which turns out to be larger in compound classes with lower superconducting transition temperatures. Temperature is the only scale of the response at different doping levels demonstrating the importance of quantum critical behavior.

Neural networks present characteristics where the results are strongly dependent on the training data, the weight initialisation, and the hyperparameters chosen. The determination of the distribution of a statistical estimator, as the Mean Squared Error (MSE) or the accuracy, is fundamental to evaluate the performance of a neural network model (NNM). For many machine learning models, as linear regression, it is possible to analytically obtain information as variance or confidence intervals on the results. Neural networks present the difficulty of not being analytically tractable due to their complexity. Therefore, it is impossible to easily estimate distributions of statistical estimators. When estimating the global performance of an NNM by estimating the MSE in a regression problem, for example, it is important to know the variance of the MSE. Bootstrap is one of the most important resampling techniques to estimate averages and variances, between other properties, of statistical estimators. In this tutorial, the application of resampling techniques (including bootstrap) to the evaluation of neural networks’ performance is explained from both a theoretical and practical point of view. The pseudo-code of the algorithms is provided to facilitate their implementation. Computational aspects, as the training time, are discussed, since resampling techniques always require simulations to be run many thousands of times and, therefore, are computationally intensive. A specific version of the bootstrap algorithm is presented that allows the estimation of the distribution of a statistical estimator when dealing with an NNM in a computationally effective way. Finally, algorithms are compared on both synthetically generated and real data to demonstrate their performance.

We performed Raman scattering experiments on superconductivity-induced features in ${\mathrm{Bi}}_2{\mathrm{Sr}}_2\left({\mathrm{Ca}}_{1-\mathit{x}}{\mathrm{Y}}_{\mathit{x}}\right){\mathrm{Cu}}_2{\mathrm{O}}_{8+\delta }$ (Bi-2212), ${\mathrm{YBa}}_2{\mathrm{Cu}}_3{\mathrm{O}}_{6+\mathit{x}}$ (Y-123), and ${\mathrm{Tl}}_2{\mathrm{Ba}}_2\mathrm{Cu}{\mathrm{O}}_{6+\delta }$ (Tl-2201) single crystals. The results in combination with earlier ones enable us to systematically analyze the spectral features in the doping range $0.07⩽p⩽0.24$. In $B_{2g}$ ($xy$) symmetry, we find universal spectra and the maximal gap energy ${\Delta }_0$ to scale with the superconducting transition temperature $T_c$. The $B_{1g}$ ($x^2-y^2$) spectra in all three compounds show an anomalous increase of the intensity toward overdoping. The energy scale of the corresponding peak is neither related to the pairing energy nor to the pseudogap, but possibly stems from a symmetry breaking transition at the onset point of superconductivity at $p_{\mathrm{sc}2}\simeq 0.27$.